governing-field-lifecycle

Distinguishing null versus absence in a Pydantic API schema requires enforcing field lifecycle semantics that clearly define whether a field is intentionally null or simply omitted. This prevents confusing API interactions and schema inconsistencies by applying explicit field behavior annotations.

Preventing mass assignment vulnerabilities in a Pydantic API is best achieved by enforcing schema separation between input and output models. By marking fields like `id` as OUTPUT_ONLY, you ensure clients cannot send or modify restricted fields during creation or update operations.

Mapping Pydantic models to OpenAPI 3.1 nullability involves applying specific field behavior annotations to govern output rules and input rules. This ensures your API documentation accurately reflects whether a field is required, optional, or explicitly accepts null values.

Configuring immutable fields for API updates using Pydantic involves applying IMMUTABLE field behavior annotations and utilizing distinct schema models for patch or update operations. This protects fields like `slug` from being modified after the initial resource creation.

Separating input and output Pydantic models improves API security by strictly governing field lifecycle semantics for each operation. This schema separation prevents clients from injecting sensitive data into input payloads while controlling exactly what is exposed in responses.

Mismatched nullability between an API response schema and OpenAPI documentation occurs when Pydantic models lack enforced field lifecycle semantics. Correctly mapping output rules and field behavior annotations resolves these inconsistencies for robust API documentation.